Deep Learning Applied on Refined Opinion Review Datasets
Deep Learning has been successfully applied in hard to solve areas, such as image recognition and audio
classification. However, Deep Learning has not yet reached the same performance when employed in textual data,
including Opinion Mining. In models that implement a deep architecture, Deep Learning is characterized by the
automatic feature selection step. The impact of previous data refinement in the pre-processing step before the
application of Deep Learning is investigated to identify opinion polarity. This refinement includes the use of a
classical procedure of textual content and a popular feature selection technique. The results of the experiments
overcome the results of the current literature with the Deep Belief Network application in opinion classification.
In addition to overcoming the results, their presentation is broader than the related works, considering the change
of parameter variables. We prove that combining feature selection with a basic preprocessing step, aiming to
increase data quality, might achieve promising results with Deep Belief Network implementation.
Copyright (c) 2018 Iberamia & The Authors
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Open Access publishing.
Lic. under Creative Commons CC-BY-NC
Inteligencia Artificial (Ed. IBERAMIA)
ISSN: 1988-3064 (on line).
(C) IBERAMIA & The Authors